Abstract: Superalloy casting is critical in aerospace and related fields, yet it remains challenging due to complex processes, multi-physics coupling, and difficulties in defect control such as shrinkage porosity and hot cracks. Traditional methods rely on empirical trial-and-error, resulting in low efficiency and high cost, while existing digital technologies suffer from data heterogeneity and limited model generality. To address these issues, this study introduces domain ontology knowledge to construct an ontology model for the metal manufacturing domain based on the IOF industrial ontology framework, enabling standardized semantic representation of process knowledge. On this basis, a digital model of the investment casting process is developed, integrating full-chain Internet-of-Things data for real-time simulation and optimization. Validated with a superalloy blade casting case, the model identifies root causes like pouring temperature fluctuations (±15℃) and slurry viscosity (>500 cP). After parameter adjustment, the product qualification rate increases from 40% to 66%, while the incidence of slag inclusion and delamination defects decrease by 58% and 37%, respectively. This work offers both theoretical and practical support for intelligent transformation in superalloy casting.